Paper Info
Title
Multiperspective Graph-Theoretic Similarity Measure.
Abstract
Determining the similarity between two objects is pertinent to many applications. When the basis for similarity is a set of object-to-object relationships, it is natural to rely on graph-theoretic measures. One seminal technique for measuring the structural-context similarity between a pair of graph vertices is SimRank, whose underlying intuition is that two objects are similar if they are connected by similar objects. However, by design, SimRank as well as its variants capture only a single view or perspective of similarity. Meanwhile, in many real-world scenarios, there emerge multiple perspectives of similarity, i.e., two objects may be similar from one perspective, but dissimilar from another. For instance, human subjects may generate varied, yet valid, clusterings of objects. In this work, we propose a graph-theoretic similarity measure that is natively multiperspective. In our approach, the observed object-to-object relationships due to various perspectives are integrated into a unified graph-based representation, stylised as a hypergraph to retain the distinct perspectives. We then introduce a novel model for learning and reflecting diverse similarity perceptions given the hypergraph, yielding the similarity score between any pair of objects from any perspective. In addition to proposing an algorithm for computing the similarity scores, we also provide theoretical guarantees on the convergence of the algorithm. Experiments on public datasets show that the proposed model deals better with multiperspectivity than the baselines.
Year
DOI
Venue
2018
10.1145/3269206.3271758
CIKM
Field
DocType
ISBN
Similarity learning,Convergence (routing),Graph,Information retrieval,Vertex (geometry),Similarity measure,Computer science,Hypergraph,Theoretical computer science,Perception,SimRank
Conference
978-1-4503-6014-2
Citations 
PageRank 
References 
0
0.34
11
Authors
2
Name
Order
Citations
PageRank
Dung Le194.21
Hady Wirawan Lauw280957.64